中国保费收入主要影响因素分析.docVIP

中国保费收入主要影响因素分析一、研究的目的要求保险作为金融行业的四大支柱之一，同时也是国民经济的重要组成部分，其成长壮大对与国民经济的健康发展有重要意义。近年来，我国保费收入快速增长。但是我国的保险深度和保险密度还处于世界的低水平。同时，我国保险市场结构严重不均衡，区域化差异非常大。因此研究保费收入的影响因素，有利于研究保险业的发展空间，对保险业的发展以及宏观经济的发展有重大的意义。二、 模型设定及其估计通过分析，影响中国保费收入的主要因素有：1、总人口（gross population）.用P表示，包括城镇人口和农村人口，将其引入模型用来反映人口数量对保费收入的影响。2、居民可支配收入（disposable income）,用I表示，它等于城镇居民人均可支配收入*城镇人口+农村居民人均纯收入*农村人口。将其引入模型来反映居民的支付能力以及经济发展的整体水平，将其引入模型可以观察收入对保费收入的影响。3、城乡居民储蓄存款余额（saving deposit balance of citizen and country inhabitant）,用S表示，反映居民的储蓄倾向和金融资源数量，将其引入模型可以观察储蓄对保险的替代和收入效应。为此设定了如下形式的计量经济学模型 其中，为保费收入，为城乡居民储蓄存款余额，为总人口，为居民可支配收入数据收集如下：我国保费收入/亿元城乡居民储蓄存款/亿元总人口/万人城镇居民可支配收入/亿元1993456.87127295.351185172577.41994376.41548321.271198503496.21995453.31799792.7712112142831996528.333311744.11223894838.91997772.709413724.71236265160.319981255.968715952.11247615425.119991406.171218078.212578658542000159819429.912674362802001210922117.71276276859.62002305426272.91284537702.82003388030949.11292278472.22004431834374.81299889421.62005493239755.1130756104932006564044960.313144811759.52007703645813.613212913785.82008978454621.61328021578120091113765834.813347417175二、估计参数利用Eviews软件，生成、等数据，采用这些数据对模型进行OLS回归，结果如下：Dependent Variable: YMethod: Least SquaresDate: 12/15/11 Time: 13:53Sample: 1993 2009Included observations: 17VariableCoefficientStd. Errort-StatisticProb. C24880.216244.7693.9841680.0016X20.1207320.0415912.9028020.0123X3-0.2275360.051949-4.3800130.0007X40.5046230.1610223.1338770.0079R-squared0.990798 Mean dependent var3455.164Adjusted R-squared0.988675 S.D. dependent var3330.401S.E. of regression354.4213 Akaike info criterion14.78117Sum squared resid1632988. Schwarz criterion14.97722Log likelihood-121.6400 F-statistic466.5920Durbin-Watson stat1.206695 Prob(F-statistic)0.00000024880.210.120732-0.227536+0.504623 （6244.769）（0.041591）（0.051949）（0.161022） t （3.984168）（2.902802）（-4.380013）（3.133877）0.990798 0.988675 466.5920 DW=1.206695经济意义检验。在假定其他条件不变的情况下，城乡居民储蓄存款余额每增长1亿元，保费收入增长0.120732亿元；居民可支配收入每增长1亿元，保费收入增长0.504623亿元。这与理论分析和经验判断相一致。拟合优度：从回归的结果来看，0.990798 0.988675 ，这说明模型对样本的拟合很好。检验：针对：0，给定的显著性水平0.05，在分布表中查出自由度为13和12的临界值，由回归结果中得到的明显大于，应拒绝原假设H0：0，说明回归方程显著，即“城乡居民储蓄存款余额”、“总人口”“ 居民可支配收入”等变量联合起来确实对“保费收入”有显著影响。检验：分别针对：0（=1，2，3，4），给定的显著性水平0.05，在分布表中查出自由度为=12的临界值（）2.179。由回归结果中的数据可得， 与、对应的统计分别为3.984168、2.902802、-4.380013、3.133877，其绝对值大于（）2.179，这说明在显著性水平0.05下，分别都应当拒绝：0（=1，2，3，4），也就是说，当在其他解释变量不变的情况下，解释变量城乡居民储蓄存款余额”、“总人口”“ 居民可支配收入”分别对被解释变量“保费收入”有显著影响。三、多重共线性检验 相关系数矩阵X2X3X4X2 1.000000 0.930923 0.991817X3 0.930923 1.000000 0.923753X4 0.991817 0.923753 1.000000由于关系系数矩阵可以看出，各解释变量互相之间的相关系数较高，正席确实存在严重多重共线性。四、多重共线性修正采用逐步回归的办法，去检验和解决冬虫共线性问题。分别作对、的一元回归，回归结果如下，Dependent Variable: YMethod: Least SquaresDate: 12/15/11 Time: 14:11Sample: 1993 2009Included observations: 17VariableCoefficientStd. Errort-StatisticProb. C-1717.426273.6313-6.2764240.0000X20.1874770.00844722.195610.0000R-squared0.970452 Mean dependent var3455.164Adjusted R-squared0.968482 S.D. dependent var3330.401S.E. of regression591.2570 Akaike info criterion15.71251Sum squared resid5243773. Schwarz criterion15.81053Log likelihood-131.5563 F-statistic492.6452Durbin-Watson stat0.739121 Prob(F-statistic)0.000000Dependent Variable: YMethod: Least SquaresDate: 12/15/11 Time: 14:12Sample: 1993 2009Included observations: 17VariableCoefficientStd. Errort-StatisticProb. C-75756.7511334.35-6.6838180.0000X30.6238030.0892036.9931060.0000R-squared0.765271 Mean dependent var3455.164Adjusted R-squared0.749623 S.D. dependent var3330.401S.E. of regression1666.457 Akaike info criterion17.78492Sum squared resid41656170 Schwarz criterion17.88294Log likelihood-149.1718 F-statistic48.90353Durbin-Watson stat0.245450 Prob(F-statistic)0.000004Dependent Variable: YMethod: Least SquaresDate: 12/15/11 Time: 14:12Sample: 1993 2009Included observations: 17VariableCoefficientStd. Errort-StatisticProb. C-2800.139288.7317-9.6980660.0000X40.7630310.03137424.320470.0000R-squared0.975267 Mean dependent var3455.164Adjusted R-squared0.973618 S.D. dependent var3330.401S.E. of regression540.9366 Akaike info criterion15.53461Sum squared resid4389186. Schwarz criterion15.63264Log likelihood-130.0442 F-statistic591.4853Durbin-Watson stat0.703141 Prob(F-statistic)0.000000对回归结果进行整理，如下表 一元回归估计结果变量参数估计值0.1874770.6238030.763031统计量22.195616.99310624.320470.9704520.7652710.9752670.9684820.7496230.973618其中，加入的方程最大，以为基础，顺次加入其他变量逐步回归。结果如下表所示。Dependent Variable: YMethod: Least SquaresDate: 12/15/11 Time: 14:18Sample: 1993 2009Included observations: 17VariableCoefficientStd. Errort-StatisticProb. C-2443.189434.1735-5.6272170.0001X40.4978570.2441322.0392950.0608X20.0658540.0601321.0951550.2919R-squared0.977219 Mean dependent var3455.164Adjusted R-squared0.973965 S.D. dependent var3330.401S.E. of regression537.3772 Akaike info criterion15.57006Sum squared resid4042840. Schwarz criterion15.71710Log likelihood-129.3455 F-statistic300.2730Durbin-Watson stat0.626973 Prob(F-statistic)0.000000Dependent Variable: YMethod: Least SquaresDate: 12/15/11 Time: 14:18Sample: 1993 2009Included observations: 17VariableCoefficientStd. Errort-StatisticProb. C18830.197282.5122.5856720.0216X40.9453060.06640014.236620.0000X3-0.1821090.061281-2.9717090.0101R-squared0.984834 Mean dependent var3455.164Adjusted R-squared0.982667 S.D. dependent var3330.401S.E. of regression438.4590 Akaike info criterion15.16319Sum squared resid2691448. Schwarz criterion15.31023Log likelihood-125.8872 F-statistic454.5566Durbin-Watson stat0.957703 Prob(F-statistic)0.000000加入时，有所提高，但t检验变得不显著，应予以剔除。加入，它的系数与经济意义不符，也应予以剔除。所以只保留。 最后修正严重多重共线性影响后的回归结果为-2800.139 0.763031 （288.7317）（0.031374）（-9.698066）（24.32047）0.975267 0.973618591.4853 DW=0.703141这说明，当居民可支配收入每增长1亿元，保费收入增长0.763031亿元。五、异方差检验利用eviews绘制出对的散点图，图如下，由图可以看出，残差平方随解释变量的变动呈增大趋势，因此，模型很有可能存在异方差。但是否确实存在异方差还应通过更进一步的检验。 再进行white检验，回归检验结果如下，White Heteroskedasticity Test:F-statistic5.785741 Probability0.014744Obs*R-squared7.692757 Probability0.021357Test Equation:Dependent Variable: RESID2Method: Least SquaresDate: 12/15/11 Time: 14:43Sample: 1993 2009Included observations: 17VariableCoefficientStd. Errort-StatisticProb. C1446298.390877.13.7001350.0024X4-307.892492.63325-3.3237790.0050X420.0157740.0046383.4014690.0043R-squared0.452515 Mean dependent var258187.4Adjusted R-squared0.374303 S.D. dependent var407216.3S.E. of regression322112.2 Akaike info criterion28.36197Sum squared resid1.45E+12 Schwarz criterion28.50901Log likelihood-238.0768 F-statistic5.785741Durbin-Watson stat1.279220 Prob(F-statistic)0.014744由上表可知，7.692757，在=0.05下，查表得，临界值为3.84146 而3.84146，表明模型存在异方差。六、异方差修正：运用WLS估计法，经估计检验发现用权数W3的效果最好，下面仅给出W3的结果；Dependent Variable: YMethod: Least SquaresDate: 12/15/11 Time: 14:57Sample: 1993 2009Included observations: 17Weighting series: W3VariableCoefficientStd. Errort-StatisticProb. C-2312.455294.1171-7.8623600.0000X40.7035430.04091217.196560.0000Weighted StatisticsR-squared0.919536 Mean dependent var2732.370Adjusted R-squared0.914172 S.D. dependent var2055.968S.E. of regression602.3242 Akaike info criterion15.74960Sum squared resid5441916. Schwarz criterion15.84762Log likelihood-131.8716 F-statistic171.4199Durbin-Watson stat0.606286 Prob(F-statistic)0.000000Unweighted StatisticsR-squared0.969339 Mean dependent var3455.164Adjusted R-squared0.967295 S.D. dependent var3330.401S.E. of regression602.2839 Sum squared resid5441188.Durbin-Watson stat0.596653-2312.455+0.703543 (294.1171.) (0.040912) （-7.862360）（17.19656）0.969339 0.914172171.4199 DW=0.606286可以看出运用加权最小二乘法消除了异方差后，参数的t检验均显著，可决系数大幅提高，F检验也显著。七、自相关检验Dependent Variable: YMethod: Least SquaresDate: 12/15/11 Time: 14:12Sample: 1993 2009Included observations: 17VariableCoefficientStd. Errort-StatisticProb. C-2800.139288.7317-9.6980660.0000X40.7630310.03137424.320470.0000R-squared0.975267 Mean dependent var3455.164Adjusted R-squared0.973618 S.D. dependent var3330.401S.E. of regression540.9366 Akaike info criterion15.53461Sum squared resid4389186. Schwarz criterion15.63264Log likelihood-130.0442 F-statistic591.4853Durbin-Watson stat0.703141 Prob(F-statistic)0.000000-2800.139 0.763031 （288.7317）（0.031374）（-9.698066）（24.32047）0.975267 0.973618591.4853 DW=0.703141对样本量为17、一个解释变量的模型、0.05显著水平，查DW统计表可知，1.133，1.381，模型中DW=0.703141，说明模型存在自相关。八、自相关修正：生成残差序列e，使用e进行滞后一期的自回归，回归结果如下：Dependent Variable: EMethod: Least SquaresDate: 12/15/11 Time: 15:18Sample(adjusted): 1994 2009Included observations: 16 after adjusting endpointsVariableCoefficientStd. Errort-StatisticProb. E(-1)0.6791250.2316852.9312450.0103R-squared0.356072 Mean dependent var-59.75093Adjusted R-squared0.356072 S.D. dependent var545.8999S.E. of regression438.0581 Akaike info criterion15.06304Sum squared resid2878424. Schwarz criterion15.11133Log likelihood-119.5043 Durbin-Watson stat0.946006对原模型进行广义差分，再对广义差分方程进行自回归，回归结果如下